In the context of driving top-line growth, businesses develop “Business Cases”. A business case includes a multi-period financial plan, in terms of a projected time-profile of all select financial metrics, such as Revenue, Gross Profit Margin, Development Expense, Sales & General Administration Expenses, etc. A business case proposal is typically built using nominal, deterministic, point-value estimates for the projected time-profiles of all select financial metrics. In order to perform a risk-adjusted portfolio analysis of a proposed set of business cases, each business case needs to be analyzed with respect to various risk factors, and the deterministic, nominal estimates of all projected financial metric time-profiles need to be converted into suitable probability distributions.
While techniques and processes exist that address the problem of business-case portfolio analysis, all of them expect the risk-information as an input into the portfolio analysis process/technique. The known techniques for risk-adjusted portfolio analysis of business cases range from requiring the user to input a risk metric score, e.g., on a scale of one to ten, for each business case, to requiring the user to input a probability distribution for each financial metric projection that is considered to be risky.
The limitation in such a requirement is that most people that prepare and propose business cases cannot directly write down a net risk metric score for the business case, or directly write down a probability distribution for describing the uncertainty in the projected financial metric time-profiles. What is needed is an analytical technique that can capture their state-of-the-art beliefs about various risk factors in a format that is easy to use, and further convert the user-inputs into a risk metric score, or appropriate probability distributions.
The problem then becomes: given a business case, which includes a multi-period financial plan, how does one arrive at risk quantification for the business case? How does one develop a risk metric score, or more generally, probability distributions that describe the uncertainty in the projected financial metric time-profiles. A solution to the problem would provide a useful and effective step for performing risk-adjusted portfolio analysis of a set of business cases. In practice, this step is often the gating step, which enables, or impedes, effective risk-adjusted portfolio analysis of business cases.
Bayesian networks address the problem of reasoning and inference based on expert beliefs on conditional probabilities of various states. They require prior probabilities to be input by the user, as well as conditional probability tables to be input by the user. Further, they do not address the above problem of developing the quantification of impact distributions that can result from a combination of risk factors. In the above context of developing risk quantification for business cases, what is needed is to develop a probability distribution of the net impact to a nominal financial metric estimate that can result from a combination of risk factors.